Excited about this one! Variance reduction in Monte Carlo is really a multi-marginal optimal transport problem, and treating it as such gives us tools to sample more efficiently in Euclidean and discrete spaces arxiv.org/abs/2405.16541
Come and find out 1) why your random walks shouldn't be i.i.d., and 2) how you can use them to construct random features for graph node kernels! @kchorolab and I will be at poster sessions 2 and 5 😎
Come and find out 1) why your random walks shouldn't be i.i.d., and 2) how you can use them to construct random features for graph node kernels! @kchorolab and I will be at poster sessions 2 and 5 😎
Quasi-Monte Carlo Graph Random Features was a v fun project investigating how correlations between random walkers can improve the efficiency of sampling on graphs. I’m not in New Orleans this time round but the excellent @kchorolab will be at PS 6! arxiv.org/abs/2305.12470
Quasi-Monte Carlo Graph Random Features was a v fun project investigating how correlations between random walkers can improve the efficiency of sampling on graphs. I’m not in New Orleans this time round but the excellent @kchorolab will be at PS 6! arxiv.org/abs/2305.12470
Super stoked to be in Honolulu at #ICML2023 presenting our paper Simplex Random Features! Catch our poster (session 6) and our oral presentation (C1) on Thursday. Come to chat about quasi-Monte Carlo, scalable Transformers and the waves at Waikiki!
We're at #ICML2023 and excited to share our work! Our group and collaborators will be presenting 8 papers at the main conference and 5 papers at the workshops. Check out our @icmlconf schedule below, and please come talk to us and our fantastic collaborators :)
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